Neo4j vs Weaviate: Vector Databases Comparison
Compare Neo4j and Weaviate side by side. Both are tools in the Vector Databases category.
The short answer
Choose Neo4j if you need knowledge-augmented RAG systems.
Choose Weaviate if you need multimodal search across text, images, and more.
Want to compare Neo4j and Weaviate on your own traffic?
Respan lets you trace LLM and agent calls across any model or framework, A/B test prompts on production traffic, and route requests across 250+ models through one gateway. Free tier covers 10K traces per month. Setup in 5 minutes, no credit card.
Quick Comparison
| Category | Vector Databases | Vector Databases |
| Pricing | Freemium | Open Source |
| Best For | Enterprises that need a mature, production-grade graph database | Developers who need a flexible, open-source vector database with multimodal and hybrid search |
| Website | neo4j.com | weaviate.io |
| Key Features |
|
|
| Use Cases |
|
|
When to Choose Neo4j vs Weaviate
- Knowledge-augmented RAG systems
- Fraud detection and network analysis
- Recommendation engines
- Multimodal search across text, images, and more
- RAG with hybrid retrieval
- E-commerce product search
How to Choose a Vector Databases Tool
Key criteria to evaluate when comparing Vector Databases solutions:
About Neo4j
Neo4j is the world's leading graph database, widely used for building knowledge graphs that power AI applications. Its native graph storage and Cypher query language enable complex relationship queries, pattern matching, and path finding. Neo4j's GenAI integrations include vector search, LLM-powered knowledge graph construction, and GraphRAG capabilities that combine structured graph data with LLM reasoning for more accurate, explainable AI.
About Weaviate
Weaviate is an open-source vector database that combines vector search with structured filtering and generative capabilities. It supports multiple vectorization modules, hybrid search (combining BM25 and vector search), and built-in integrations with LLMs for retrieval-augmented generation. Weaviate offers both self-hosted and managed cloud deployments, with a GraphQL API that makes it easy to query complex data structures.
What is Vector Databases?
Purpose-built databases for storing, indexing, and querying high-dimensional vector embeddings used in semantic search, RAG, and recommendation systems.
Browse all Vector Databasestools →Frequently Asked Questions
Other Vector Databases Tools
More Vector Databases Comparisons
Run Vector Databases in production with Respan
One platform for routing, observability, tracing, and evals across every LLM provider.